The present invention relates to an apparatus and method for automatic accident analysis in an electric power system, wherein fault locations and their modes which identify the initiating disturbance and any device misoperations can be automatically inferred by a computer, using experience data and a subjective judgment input by an operator, in equipment constituting a power system.
Several computer-aided inference systems for analyzing accidents in a power system have been proposed. In general, these systems are called expert systems and use a knowledge base. In most of these conventional systems, empirical knowledge is expressed in the form of a so-called "production rule". More specifically, the expression "if accident X occurs, then fault H has occurred" is employed as a production rule. A computer receives a post-accident phenomenon and calculates one or plural fault locations and fault modes by using the knowledge base. Fault modes in a power system include three-phase short-circuiting, two-phase short-circuiting, one-phase grounding, multi-phase grounding, device misoperation and the like.
An accident cannot always be accurately/reliably defined (with certainty), and a production system cannot always reliably determine a relationship between the cause and effect of an accident. For example, an arc resistance difference for grounding at the same location changes relay operation. Uncertain elements also exist which cause non-operation or erroneous-operation of relays and circuit breakers. When these uncertain elements are considered, it is impossible to express the cause-and-effect relationship in a definite production rule.
In order to eliminate this drawback, many improvements have been made. According to one improved method, certainty factor is added to each production rule. More specifically, this method adapts a form "if accident X occurs, probability that fault H has occurred A". In this form, "A" is called a certainty factor. Extensive studies have been made to define certainty factor A.
The Bayesian probability is widely used as a definition of certainty factor A. However, it cannot express "subjectively uncertain" knowledge. (The main criticism regarding the use of the Bayesian probability to express subjective uncertainty is that it cannot be used to deal with ignorance in an effective manner. In other words, the Bayesian theory cannot distinguish between belief and disbelief.) Assume that certainty factor A is 0.6. According to the Bayesian probability, the above assumption is expressed by the following production: "If phenomenon X occurs, a probability that accident H has occurred 0.6, and a probability that accident H has not occurred is 0.4."
Most practical way of expressing uncertainty is: "If accident X occurs, the probability or belief that fault H has occurred is 0.6, and the probability for ignorance is 0.4."